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RayleighBard convection in a BoussinesqStokes ferromagnetic fluid under sinusoidal and non-sinusoidal internal heat modulation
Internal heat modulation has several applications in nuclear reactor design and safety, as well as meteorology. In this paper, the influence of internal heat modulation on RayleighBard convection in a BoussinesqStokes ferromagnetic fluid is explored using linear and nonlinear analyses. The impact of the square, sine, triangular, and sawtooth wave type of internal heat modulation on the onset of convection and heat transport is considered. Using a Venezian method, linear stability analysis is performed to derive the correction Rayleigh number and the critical Rayleigh number for all four waveforms. A nonautonomous Lorenz model is derived and solved for the amplitude to obtain the Nusselt number, which quantifies the heat transport. The impact of the nondimensional parameter on the convective onset and heat transfer under heat source/sink modulation is analyzed. The study shows that all four types of internal heat modulation destabilize the system. It is also found that the presence of a heat source/sink modulation affects the impact of all four types of internal heat modulation on heat transport. 2022 Wiley Periodicals LLC. -
Ind as convergence and its impact on value relevance of accounting variables
This study investigates the correlation between accounting variables and stock prices in India from 2011 to 2022, emphasizing the impact of the Convergence of Ind AS with IFRS. Specifically, it explores how this convergence influences the value relevance of accounting data using the Ohlson Model. Beyond established vari-ables like Earnings per Share (EPS) and Book Value (BPS), the analysis introduces additional factors such as Dividend per Share (DPS) and Research and Development (R&D). Employing Ohlsons Pricing Model (1995), the research assesses the rela-tionship between these accounting variables and stock prices for 47 consistently listed firms in the NIFTY 500 index. Results show that DPS and R&D enhance the value relevance of the Ohlson Model, with the adjusted upper R squaredupper R squared increasing from 0.2 in 2011 to 0.6 in 2022. While overall value relevance improves after Ind AS convergence, BPS remains consistent when controlling for EPS. The Author(s). -
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Comparison of HOG and fisherfaces based face recognition system using MATLAB
Face recognition and validation is not an easy task due to barriers in between like variation in pose, facial expressions and illumination. There are many algorithms available to build a face recognition system. One such popular method of approach is the Histogram of Oriented Gradients (HOG). It is a simple but effective algorithm. Even though it gives satisfactory results, it sometimes mismatches query image with irrelevant images, especially in poor lighting conditions. This paper presents a more accurate technique called Fisherfaces. It is a more reliable method for face recognition and validation. Fisherface algorithm is utilized primarily for reducing the dimensionality of the feature space. Fisherface method for image recognition involves a series of steps. Firstly, the face space dimension is reduced using Principal Component Analysis (PCA) method, then the Linear Discriminant Analysis (LDA) method is used for feature extraction. Fisherface method produced good results even under complex situations like varying illumination conditions and images with different poses and expressions which is a major drawback of HOG. Fisherface algorithm can reach a maximum accuracy of 96.87%. Error Correcting Output Code (ECOC) is the classifier used for both HOG and Fisherfaces. 2021 IEEE. -
Plant disease diagnosis and solution system based on neural networks
Plant diseases are one of the major factors affecting crop yield. Early identification of these diseases can improve productivity and save money and time for the farmer. This paper presents a novel technique to diagnose plant diseases using a mobile application. A Convolutional Neural Network (CNN) model was built and trained using MobileNetV2 architecture with the help of image processing techniques and transfer learning. A dataset comprising 87,000 images that contain 38 classes of diseases belonging to 14 different crops was used to train the model. The model achieved an accuracy of 98.69% and a loss of 0.5373. A mobile application was built in Android Studio with the help of a trained model. The mobile application built works without a need for a remote server. The application can identify the disease, gives information regarding the identified disease and also suggests necessary remedies to tackle the disease. 2021, Engg Journals Publications. All rights reserved. -
Farm food and beverage: An attractive element of gastronomy in agritourism
The admixture of agriculture and tourism creates new fields of area like agritourism. The primary activity of agritourism is providing unique agritourism attractions to the visitors. Among the interests, farm food and beverages act as substantial components that intrigue the visitors. Food gastronomy is connected with farm food and beverage and its inception. Tourists in the agritourist destination want to explore the culinary practices there. Hence, this book chapter provides an idea of the concepts of agritourism and gastronomy, the applications of gastronomy in agritourism, the significance and dimensions of farm food and beverage in agritourism, factors influencing travelers' food choices, the benefits of gastronomy in agritourism and the value-added advantage of gastronomy in agritourism. Food is always a determinant element of the quality of service in the tourist place. 2024, IGI Global. All rights reserved. -
Electrochemical sensing of vitamin B6 using platinum nanoparticles decorated poly(2-aminothiazole)
Vitamin B6 (Vit B6), also known as pyridoxine, is pivotal in fundamental physiological and metabolic processes within the body. Insufficient levels of this essential nutrient may contribute to various health complications. We introduce an electrochemical sensor designed to determine Vit B6 levels precisely. This sensor is constructed through a two-step process: first, by modifying a bare carbon fiber paper electrode (CFP) with poly(2-aminothiazole) (PAT), and second, by electrodepositing platinum nanoparticles onto the modified electrode surface, giving the final working electrode- Pt/PAT/CFP. Electrochemical impedance spectroscopy (EIS) and Cyclic voltammetry (CV) were utilized to examine the electrochemical characteristics of the developed sensor. The characterization of the sensor was done through a range of analytical techniques, including X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and optical profilometric studies. Furthermore, we optimized the sensor's performance by assessing the impact of pH, scan rates, and analyte concentrations. The sensor showed a wide linear dynamic range of 5.0 nM80 M and a low detection limit of 0.054 M. We have successfully quantified Vit B6 levels in tablet formulations and dried palm date fruits. The outcomes of this study hold the promise of substantial progress in Vit B6 quantification, with far-reaching implications across pharmaceuticals, healthcare, and nutritional science. 2024 Elsevier B.V. -
Review on the fish collagen-based scaffolds in wound healing and tissue engineering
Collagen from marine organisms is an emerging source in tissue engineering, an alternative to bovine and porcine collagen. Despite the positive results on wound healing from fish collagen scaffolds, the comprehension and advancements in its properties are limited. Given this context, this study aimed to carry out a systematic review to examine the effects of collagen scaffolds on different models of experimental skin wounds, the advantages and its limitations. The search was conducted according to the orientations of Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) and the MeSH (Medical Subject Healings) terms used were Fish Collagen AND Wound Healing AND Scaffold. 36 articles in total were sorted out from the databases of Google Scholar and PubMed. After the analysis, the current review covers 10 articles from the beginning of 2017 to March 2023. The results are mainly focused on the different methods of collagen extraction, preparation of scaffolds and its treatment on the animal model along with its effects. To infer, this current review states that, despite the positive effects of collagen on tissue repair and regeneration, no product is available for medical purposes. Thus, this review also demonstrates the huge potential for collagen in tissue engineering. 2024 Visagaa Publishing House. -
Agro-food traceability with efficient user interface using blockchain technology
Food traceability is crucial for food quality and safety to reduce vulnerabilities of product globalization. The traditional Agri-food production system does not offer easy traceability of the product at any point of the supply chain. Blockchain based production system resolves the challenges by reducing the complexity of traceability. Still no other study has presented Blockchain-based traceability platform with a lower impact on the environment and lower cost for each transaction sent by the supply chain. In the existing system, proof of work consensus protocol is used in blockchain which consumes more energy for transactions. The proposed traceability system is based on Ethereum Blockchain, which uses the Proof-of-Stake mechanism of consensus that requires minimal computational power, is highly scalable and environmentally sustainable. The user interface of consumer is specially designed that provides all the tracking information of the agro-food. The developed traceability platform digitizes the entire production chain making the data immutable and available in realtime. 2024 by IGI Global. -
Wide band cascade RF amplifier for 0.01GHz to 6 GHz application
This paper presents a design of wide band cascade RF amplifier for 0.01 GHz to 6 GHz application using Hybrid Microwave Integrated (MIC) Technology. Wideband amplifier provides ultra-flat gain response of 1 dB for 4 GHz bandwidth and 3 dB for 6 GHz bandwidth. A coplanar wave guide (CPWG) is fabricated using printed circuit board technology and used for RF transmission line topology to convey microwave frequency signals. The output power at 1 dB compression is 17 dBm while the high gain is 22 dBm. The return loss shows below minimum -10 dB for all frequency and amplifier have good linearity and stability. The proposed amplifier can be used for L, S, and C band applications. 2019 IEEE -
A protoberberine alkaloid based ratiometric pH-responsive probe for the detection of diabetic ketoacidosis
Herein we report a ratiometric naturally occurring fluorescent pH probe, berberrubine (BBn) for the direct detection of diabetic ketoacidosis (DKA) conditions of patients having type I diabetes mellitus. The photophysical properties of the probe during pH titrations showed remarkable changes in absorption spectra where two absorption bands at 377 and 326 nm have disappeared followed by the emergence of an absorption maxima at 346 nm in highly acidic conditions. In addition, a fluorescence enhancement effect was observed in the alkaline pH, with a bathochromic shift of 33 nm. Moreover, the solution switches the color from light yellow to light pink with the change of pH from acidic to basic. A pKa value of 7.57 and a good linearity between pH 5.09.0 indicate that the probe can be used efficiently for the DKA condition, where pH variations are in the range of 67. The excellent water solubility, photostability, reversibility, and selectivity of BBn make it a potential pH sensing agent for acidic microenvironments. The reversible sensing of pH variations during DKA could be effective in primary detection and diagnosis which can assist in avoiding further complications of acidosis. 2021 Elsevier Ltd -
A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification; [Un modelo marco progresivo UNDML para el diagntico y clasificaci del ccer de mama]
According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the diseases fatality rate. Early cancer detection raises the chance of survival by up to 8 %. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy. 2024; Los autores. -
Solar Mapping of India using Support Vector Machine
Accurate knowledge of global solar radiation (GSR) data is necessary for various solar energy based applications. However, in spite of its importance, the number of solar radiation measuring stations is comparatively rare throughout the world due to financial cost and difficulties in measurement techniques. The objective of this current study is to assess the solar energy potential and to develop solar resource mapping of India without utilizing the direct measurement techniques. GSR is predicted with commonly available meteorological parameters like minimum and maximum temperature as its inputs by using Support Vector Machine (SVM) based solar radiation model. The SVM model is validated with measured data from India Meteorological Department (IMD). This study simplifies the major challenge of preparing GSR data for various solar energy applications in a big country like India. Also the life cycle cost of Solar PV is analyzed in India. The payback period will be around 3 years for an annually solar radiation of range from 3.5 to 6 kWh/m 2 /day. This work eliminates the requirement of costly pyranometer to get GSR data. Solar resource mapping of India is developed without direct measurement technique thus avoids GSR data recording, daily maintenance and subsequently the increasing cost of GSR data collection. 2018 Web Portal IOP. All rights reserved. -
Users Perception and Barriers to Using Self-Driven Rental Bikes
The research study has two objectives. The first objective of this paper was to find users' perception towards self-drive rental bikes. The second objective was to identify the factors that act as barriers to users using self-drive rental bikes. The research was a formal and structured conclusive research type and used quantitative data analysis techniques. The study had a representative sample of 350 respondents. The population selected for this study were people of various demographics in Bangalore. We used judgemental sampling to decide on the right sample. In achieving both objectives, factor analysis was used to arrive at a minimum number of factors or dimensions. The major perception factors are: Economical Choice, Environmental Consciousness, Alternative Source of Transport, Rationality, and Convenience. The major barriers to using self-drive rental bikes are Safety Issues, Conservative Nature of Users, the Expensive Nature of Service, and the Difficulty in Using Mobile Applications. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Research Advancements In Autism Spectrum Disorder Using Neuroimaging
Autism Spectrum Disorder (ASD) is a complex neurological condition that manifests as a spectrum of symptoms at varying levels of severity.. Insufficient data and heterogeneous characteristics of ASD are the primary causes of it being a complex, challenging, and intriguing field of research. ASD is declared one of the fastest-growing mental disorders affecting the normal life of subjects at various levels of severity and stages of age. Recent research work observed a significant change in brain structure, functional connectivity, and network using neuroimaging resources. Each autistic brain is as unique as a fingerprint for typically developed subjects. Magnetic Resonance Imaging (MRI) is accepted as an excellent diagnostic technology for numerous disorders with a satisfactory amount of information by medical experts. Cognitive deficits brain MRI modalities contain microscopic information, which is time-consuming and needs experts to interpret. Artificial intelligence (AI) strategies (Machine Learning and Deep Learning) are implemented with various imaging modalities to decrypt the information for diagnosis and to support computer-added solutions for appropriate treatment. The research aims to discover the various evolutionary impacts of artificial intelligence for the diagnosis of Autism syndrome disorder using neuroimaging. To automate the diagnosis using artificial intelligence methodologies, medical imaging has proved to be of immense use. Though neuroimaging and AI produced satisfactory diagnostic solutions for many mental disorders, research is required to explore the autistic brain for more neuroimaging information to be used for further investigation. Some of the Internet of Things (IoT) solutions for detection and training are also invented but not with the use of Neuroimaging. Autism is a neurological condition that affects the brain, and hence more research is advised using imaging and AI techniques to support the community to enjoy a normal life. 2023 American Institute of Physics Inc.. All rights reserved. -
Implementing a programmable drop voltage controller vlsi
This study offers a new synchronized practice area door array (FPGAs), to minimize electricity usage. Concurrent bit-serial architecture is shown in the figure to minimize energy consumption and timing synchronization of switching structures. Researchers offer a fine-grained energy control system with each Look-up database to minimize the Static energy by the channel length, which is now equivalent to the dynamical one (LUT). A 90 nm Processor is the planned field-programmable VLSI. Its electricity consumption is 42 percent lower than that of sequential design. 2021, SciTechnol, All Rights Reserved. -
Novel splitring resonator antennas for biomedical application
Our paper presents the design and development of split ring resonator based metamaterial antenna for biomedical i.e., Industrial, Scientific and Medical(ISM-2.45GHz) applications and also used in biosensors. Now a day the biological changes in the human body such as glucose content in blood, heart rate, respiratory rate, brain tumor are monitored by the use of wireless body area networks. In such networks the main part of the system is antenna with compactness and wider bandwidth. We have designed gain enhanced and wide bandwidth antennas with size reduction of more than 95% compared to the conventional patch antenna. The design methodology is based on Metamaterial which is an emerging technology uses split ring resonators for size reduction. We have designed double square split ring shape superstrate antenna and circular ring resonator antenna with stub for 2.48GHz. Also they have better return loss (>12dB). Our antennas are fed with microstrip feeding and Coplanar Waveguide (CPW) feeding for better impedance matching and easy fabrication. The fabricated antennas are tested using Network analyzer. The measured results are good in agreement with simulated results. 2015, Journal of Pure and Applied Microbiology. All rights reserved. -
Predicting Song Popularity Using Data Analysis
In today's music landscape, predicting a song's success is crucial for musicians, record labels, and streaming platforms. This paper introduces a methodology for estimating popularity using Spotify data, termed the 'Proxy Popularity Score.' Three models - Random Forest, LightGBM Regressor, and XGBoost Regressor - are utilized for prediction. Performance metrics including mean absolute error, mean squared error, root mean squared error, and R-squared error are employed to evaluate model accuracy. Correlation values of 99.85%, 99.87%, and 99.84% are achieved for XGBoost, LightGBM, and Random Forest respectively. The study concludes with a ranking of songs based on predicted popularity scores. 2024 IEEE. -
Understanding the developmental relevance of animated cartoons: How people perceive the United Nations productions /
The mass media has emerged as one of the greatest tools and a catalyst in propelling some of the great changes in the society. Animated cartoon is one of the forms of mass media. Although often understated, mildly acknowledged and subtle in its ways, it holds the potential to make positive changes if used optimally. -
An Intelligent Recommendation System Using Market Segmentation
Electronic commerce, sometimes known as E-Commerce, is exchanging services and goods over the internet. These E-Commerce systems generate a lot of information. To solve these Data Overload issues, Recommender Systems are deployed. Because of the change to online buying, companies must now accommodate customers needs while also providing more options. The strategies and compromises of common recommender systems will be discussed to assist clients in these situations. Recommendation algorithms generate lists of things that the user have been previously using (content filtering) or develop recommendations and analyzing what items users purchase and identify similar target users (collaborative filtering). To assist clients in these situations, The Apriori algorithm, standard and custom metrics, association rules, aggregation, and pruning are used to improve results after a review of popular recommender system strategies that have been used. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.